Data Analysis & Processing
Course Overview:
This course equips you with the foundational skills for data analysis and processing, the essential building blocks for leveraging Artificial Intelligence (AI) in Supply Chain Management (SCM). You'll gain hands-on experience wrangling, cleaning, and preparing your data for powerful AI applications, unlocking valuable insights for optimizing your supply chains.
Learning Objectives:
Understand the importance of data analysis and processing for AI applications in SCM.
Explore the various stages of the data analysis lifecycle (data collection, cleaning, transformation, analysis).
Gain hands-on experience with popular data analysis tools and techniques (e.g., Python libraries like Pandas).
Identify and address common data quality issues (missing values, outliers) relevant to SCM data.
Prepare real-world SCM data for use in AI models (e.g., demand forecasting, inventory optimization).
Course Highlights:
1. Foundations of Data Analysis for AI
Introduction to Data Analysis and AI in SCM: Understanding the role of clean data in building effective AI models.
The Data Analysis Lifecycle: Exploring the different stages of data preparation (collection, cleaning, transformation, analysis).
Introduction to Data Analysis Tools: Learning the basics of Python libraries like Pandas for data manipulation and analysis.
Hands-on Exercises: Utilizing Python libraries to explore, clean, and manipulate real-world SCM data (provided datasets).
Case Studies: Examining how data analysis helps prepare data for AI models in areas like demand forecasting or inventory optimization.
2. Wrangling Real-World SCM Data
Diving into Data Cleaning Techniques: Addressing common data quality issues like missing values, outliers, and inconsistencies.
Feature Engineering for AI Models: Transforming raw data into meaningful features for AI algorithms to utilize.
Introduction to Data Visualization: Creating informative charts and graphs to understand your cleaned and prepared data.
Hands-on Exercises: Applying data cleaning techniques and feature engineering to prepare real-world SCM data for a chosen AI task (e.g., predicting transportation delays).
Course Wrap-up: Exploring responsible data practices and potential biases in SCM data.
Prerequisites:
Basic understanding of mathematics and statistics
Familiarity with programming concepts and a language such as Python or R
Knowledge of database systems and SQL is beneficial but not required